用知识图谱和情感分析提升简历信息提取，精准匹配人才与岗位
GraphRank Pro+: Advancing Talent Analytics Through Knowledge Graphs and Sentiment-Enhanced Skill Profiling
- 构建知识图谱抽象复杂规则，统一处理简历的多样格式
- 通过技能权重字典实现人才能力的量化评估与排序
- 适合招聘、职业规划者使用，支持精准筛选与推荐
从半结构化文本（如简历）中提取信息长期面临格式多样性和内容主观性挑战。传统方法依赖特定场景的定制逻辑，而本文提出一种融合知识图谱、自然语言处理与深度学习的创新框架。通过将复杂逻辑抽象为图结构，将原始数据转化为全面的知识图谱，实现精确的信息抽取与复杂查询。我们系统构建了技能权重字典，推动精细化人才分析。该系统不仅服务于招聘人员与课程设计者，也使求职者能基于查询进行定向筛选与排名，提升匹配效率。
原文摘要 · Abstract (English)
The extraction of information from semi-structured text, such as resumes, has long been a challenge due to the diverse formatting styles and subjective content organization. Conventional solutions rely on specialized logic tailored for specific use cases. However, we propose a revolutionary approach leveraging structured Graphs, Natural Language Processing (NLP), and Deep Learning. By abstracting intricate logic into Graph structures, we transform raw data into a comprehensive Knowledge Graph. This innovative framework enables precise information extraction and sophisticated querying. We systematically construct dictionaries assigning skill weights, paving the way for nuanced talent analysis. Our system not only benefits job recruiters and curriculum designers but also empowers job seekers with targeted query-based filtering and ranking capabilities.
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